Data Science Books: Your Essential Reading List
Must-Read Data Science Books for 2025
Rapid Fire Book Recommendations
- Couldn’t live without: Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python By Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili https://amzn.to/3B6sp3a (Practically a Python data science handbook).
- Most thought provoking: Burn Math Class By Jason Wilkes https://amzn.to/44rCroP
- Best for learning Python: Automate the Boring Stuff with Python, 3rd Edition 3rd Edition By Al Sweigart https://amzn.to/3B8zHTV (Great for learning the programming language Python).
- Most underrate: Introduction to Graph Theory 2nd Edition By Richard Trudeau https://amzn.to/4f1CgVW
- Best non-data scientist author: Safiya Noble of Algos of Oppression
- Favorite chapter: Chapter 8 of Confident Data Science “A Weekend Crash Course.” https://amzn.to/3JUgvuo (A full data science course)
- Best humanistic (humanities) research:Visualizing History’s Fragments: A Computational Approach to Humanistic Research By Ashley R Sanders https://amzn.to/3B0szJj
- Best audio book: How Data Happened By Chris Wiggins, Matthew L. Jones https://amzn.to/4eWcEtn (Really good for data science managers).
Make 2025 The Year to Read More About Your Data Science Career Options + Advancement
I am updating my list of recommended books in data science for 2025. An earlier version of this list included books from authors I know personally and authors I’d like to know. This year I continue with that breakout but also add sections related to ethics in artificial intelligence and responsible artificial intelligence and machine learning.
I’m also adding a section of textbooks (or textbook like) data science books that touch on beginner or intermediate data visualization, Pandas, Python, machine learning, and other import data tools. These books will add to or enhance your data science journey.
If you’re not sure which data science books to read first or next this list will be your go to.
FROM ✍🏾 AUTHORS ✍🏾 I DO ✍🏾 KNOW
Data Analysis with Pandas By Stefanie Molin
A comprehensive guide for efficiently performing data collection, wrangling, analysis, and visualization using Python’s pandas library. Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, tackles complex problems in information security, particularly those involving data wrangling, visualization, and tool development for data gathering. Her extensive experience in data science and machine learning, combined with her frequent presentations at Python and data conferences, has made this book a popular read among data enthusiasts. Recommended for data engineers, data analysts, those interested in data visualization, are others. https://amzn.to/3rrqi4R
Qubit Before Christmas By Frank La Vigne
This one doesn’t teach quantum data processing but rather “The Qubit Before Christmas: A Post-Modern Take on a Holiday Classic” by Frank La Vigne is a quantum-inspired rendition of the traditional holiday poem, offering a unique twist for enthusiasts of quantum computing. La Vigne, known for his work in data science and AI, brings a fresh perspective to a beloved classic. This creative piece has been featured on platforms like DataDriven.tv, where the author himself narrates the poem. Recommended for anyone working with or adjacent t the field of data science. https://amzn.to/3JUCCRw
Ace The Data Science Interview By Nick Singh
This one by Nick Singh and Kevin Huo is a comprehensive guide designed to help aspiring data professionals build data science skills and prepare for interviews at leading tech companies and financial firms. The book features “real interview questions” from major employers and organizations in the field — each accompanied by detailed, step-by-step solutions. It also offers practical advice on crafting resumes, developing impactful portfolio projects, and excelling in behavioral interviews, making it a valuable resource for those aiming to secure roles in data science, data analysis, or machine learning. I you already have, or aspire for, a data science career this book is a must for your library. https://amzn.to/3Da9TnL
Data Science For Dummies By Lillian Pierson, P.E.
Reading this book won’t just teach you data science techniques, it’ll teach you to analyze data and about business intelligence, programming languages, machine learning models, advanced data science techniques, common data science tools, the data science process, big data, deep learning, and more. The book emphasizes practical business applications, offering insights into how data science can drive value across various industries. Pierson, a seasoned data science consultant and CEO of Data-Mania, has educated over a million professionals in AI and data science, and her expertise is reflected in the comprehensive yet approachable content of this guide. https://amzn.to/3pEfDmP
AUTHORS ✍🏾 I’D ✍🏾 LIKE ✍🏾 TO✍🏾 KNOW
In this category you might notice a theme. The common theme across these four books — Burn Math Class, Algorithms of Oppression, Weapons of Math Destruction, and Unmasking AI — is the critical yet inclusive examination of math and data science.
Each book challenges readers, whether seasoned data scientists or professionals learning data science, to rethink the foundations of these fields, uncovering biases and inequities while advocating for ethical and inclusive approaches to data science techniques. Additionally, these books touch on foundational components like data engineering, which supports various applications such as machine learning and data visualization.
Burn Math Class By Jason Wilkes
This book boldly discards the conventional math curriculum and invites readers to reinvent mathematical concepts from the ground up. If you’ve previously felt alienated by “math” this book is for you. By focusing on creativity over rote memorization, Wilkes equips readers with data science skills rooted in intuitive and analytical understanding, making it a useful read for anyone interested in applying mathematical methods in data science. https://amzn.to/44rCroP
Algos of Oppression By Safiya Noble, Ph.D.
Minor (very minor) spoiler alert: Safiya Umoja Noble may not label herself a data scientist. As a non-data scientist I would but Safiya Umoja Noble’s personal pile of data science knowledge up against any professional in the field. Her critical review of data science is thorough and provacative. She pulls back the curtain on how seemingly neutral search engines can perpetuate racial and gender biases. Through meticulous research, Noble reveals how algorithms often reflect and amplify societal prejudices, challenging the assumption of technology’s impartiality. https://amzn.to/3PSS3gw
Weapons of Math Destruction By Cathy O’Neil*
Cathy O’Neil exposes the dark side of big data, illustrating how mathematical models can entrench discrimination and social injustice. With transparency and accountability central to her message, this book serves as a wake-up call for data scientists working in fields like business intelligence and programming. https://amzn.to/3NRLJn6
Unmasking AI by Joy Buolamwini*
Joy Buolamwini offers a poignant exploration of the biases embedded in artificial intelligence systems. This book discusses how data science professionals “unintentionally” (or perhaps not) encode and amplify biases. Through a combination of personal experience and rigorous research, she highlights the importance of ethical AI development and the human side of machine learning. Drawing from her experiences and research, Buolamwini advocates for ethical AI development that respects and upholds human dignity. https://amzn.to/4gdX8KB
MY📘 BOOKS📘 TOO
How To Become A Data Scientist by Adam Ross Nelson, 📊
As the author of How To Become A Data Scientist, I wrote this book to guide aspiring professionals through the exciting and ever-evolving data science world. In this book, I outline the essential skills needed to analyze data, including mastering data visualization, understanding predictive models, and diving into data modeling, all while exploring key programming languages like Python and R. Whether you’re just beginning your data science career or pivoting from computer science or another field, this book helps you navigate the steps to secure data science jobs, tackle real-world data analytics tasks, and become a confident data scientist ready to make an impact. https://amzn.to/44nE3jx
Confident Data Science By Adam Ross Nelson, 📊
Confident Data Science is my guide to empowering both new and seasoned professionals in the data science field to take charge of their data science tasks with clarity and expertise. This book covers a range of topics, from learning data science fundamentals to advanced data analysis, machine learning techniques, and the intricacies of data modeling. I also share insights into building a successful data science career, leveraging the right tools and programming languages, and excelling in data analytics and predictive models to solve business challenges. This book is a must-read for anyone looking to thrive in the competitive data science world and land their dream data science jobs. https://amzn.to/3JUgvuo
Ethical + Responsible AI, Data Science, Machine Learning for Data Science Professionals
A “quick reference” offered without commentary.
- Oxford Handbook on AI Ethics Edited By Markus Dubber, Frank Pasquale, Sunit Das https://amzn.to/3ZxC0cM
- Patterns of Inclusion: How Gender Matters for Automation, Artificial Intelligence and the Future of Work By Elisabeth Kelan https://amzn.to/4gbbviB
- AI Discrimination In Hiring A Complete Guide — 2020 Edition By Gerardus Blokdyk https://amzn.to/4inux7s
- Algos of Oppression: How Search Engines Reinforce Racism By Safiya Noble, Ph.D. https://amzn.to/3PSS3gw
- Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy By Cathy O’Neil https://amzn.to/3NRLJn6
- Unmasking AI My Mission to Protect What Is Human in a World of Machines By Joy Buolamwini https://amzn.to/4gdX8KB
- Superintelligence: Paths, Dangers, Strategies By Nick Bostrom https://amzn.to/4idsSRF
Thanks For Reading
Are you ready to learn more about careers in data science? I perform one-on-one career coaching and have a weekly email list that helps data professional job candidates. Contact me to learn more.
Thanks for reading. Send me your thoughts and ideas. You can write just to say hey. And if you really need to tell me how I got it wrong, I look forward to chatting soon. Twitter: @adamrossnelson LinkedIn: Adam Ross Nelson.
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